Skip to main content
  • AACR Journals
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

AACR logo

  • Register
  • Log in
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Collections
      • COVID-19 & Cancer Resource Center
      • Focus on Radiation Oncology
      • Novel Combinations
      • Reviews
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

  • AACR Journals
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • My Cart

Search

  • Advanced search
Molecular Cancer Therapeutics
Molecular Cancer Therapeutics
  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
    • Reviewing
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Collections
      • COVID-19 & Cancer Resource Center
      • Focus on Radiation Oncology
      • Novel Combinations
      • Reviews
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

Article

Binarization of Microarray Data on the Basis of a Mixture Model1

Xiaobo Zhou, Xiaodong Wang and Edward R. Dougherty
Xiaobo Zhou
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiaodong Wang
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Edward R. Dougherty
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
DOI:  Published July 2003
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Fig. 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Fig. 1.

    A mixture of two log-of-normal distributions.

Tables

  • Figures
  • Table 1

    Recognition accuracy (%)

    KMMBMedianMean
    1.583.7478.4178.17
    1.887.5384.5483.65
    2.089.5387.5485.94
    2.596.8996.8295.05
  • Table 2

    Recognition accuracy (no. of errors) for two cancer data sets

    MMBMeanMedian
    Breast cancer data with normalization011
    Breast cancer data without normalization013
    SRBCT with normalization001
    SRBCT without normalization043
  • Table 3

    Strongest genes selected from the quantized breast cancer data using the MMB method

    No.CloneIDGene description
    126184Phosphofructokinase, platelet
    244180α-2-macroglobulin
    3309583ESTs
    430502Reticulon 1
    5812227Solute carrier family 9 (sodium/hydrogen exchanger), isoform 1
    6376516Cell division cycle 4-like
    7137638ESTs
    8823940Transducer of ERBB2, 1 (TOB1)
    9204897Phospholipase C, γ 2 (phosphatidylinositol-specific)
    10839736Crystallin, α B
  • Table 4

    Strongest genes selected from the quantized breast cancer data using the Median method

    No.CloneIDGene description
    1812227Solute carrier family 9 (sodium/hydrogen exchanger), isoform 1
    235865Annexin A6
    3204299Replication protein A3 (14kD)
    4809981Glutathione peroxidase 4 (phospholipid hydroperoxidase)
    5245198KIAA0130 gene product
    6126412Androgen receptor associated protein 54
    748406Hydroxysteroid (17-β) dehydrogenase 4
    8712848Mitogen-activated protein-kinase activating death domain
    9814595Protein kinase C binding protein 1
    10825577Steroidogenic acute regulatory protein related
  • Table 5

    Strongest genes selected from the quantized breast cancer data using the Mean method

    No.CloneIDGene description
    1768370Tissue inhibitor of metalloproteinase 3
    2290871Integrin, α 3 (antigen CD49C, α 3 subunit of VLA-3 receptor)
    383210Complement component 8, β polypeptide
    4812227Solute carrier family 9, isoform 1
    5204299Replication protein A3 (14kD)
    6814595Protein kinase C binding protein 1
    7825577Steroidogenic acute regulatory protein related
    8126650ESTs
    9139354ESTs
    10809981Glutathione peroxidase 4 (phospholipid hydroperoxidase)
  • Table 6

    Strongest genes listed in [15]

    No.CloneIDGene description
    1897781Keratin 8
    2823940Transducer of ERBB2, 1 (TOB1)
    326184Phosphofructokinase, platelet
    4840702Selenophosphate synthetase; Human selenium donor protein
    5376516Cell division cycle 4-like
    647542Small nuclear ribonucleoprotein D1 polypeptide (16kD)
    7366647Butyrate response factor 1 (epidermal growth factor-response factor 1)
    8293104Phytanoyl-CoA hydroxylase (Refsum disease)
    928012O-linked N-acetylglucosamine (GlcNAc) transferase
    10212198Tumor protein p53-binding protein, 2
  • Table 7

    Strongest genes selected from the quantized blue-cell-tumor data using the MMB method

    No.CloneIDGene description
    1325182Cadherin 2, N-cadherin (neuronal)
    236950Phosphofructokinase, liver
    31435862Antigen identified by monoclonal antibodies 12E7, F21, and O13
    4786084Chromobox homolog 1 (Drosophila HP1 β)
    51434905Homeo box B7
    6137535Transcriptional intermediary factor 1
    7774502Protein tyrosine phosphatase, nonreceptor type 12
    8486175Solute carrier family 16 (monocarboxylic acid transporters), member 1
    9866702Protein tyrosine phosphatase, nonreceptor type 13
    10166236Glucose-6-phosphate dehydrogenase
  • Table 8

    Strongest genes selected from the quantized blue-cell-tumor data using the Median method

    No.CloneIDGene description
    136950Phosphofructokinase, liver
    2397963-Hydroxymethyl-3-methylglutaryl-Coenzyme A lyase
    3726236Paired mesoderm homeo box 1
    41434905Homeo box B7
    5814260Follicular lymphoma variant translocation 1
    638471Human cyclin G1 interacting protein (1500GX1) mRNA, complete cds
    7729964Sphingomyelin phosphodiesterase 1, acid lysosomal
    8782193Thioredoxin
    9207358Solute carrier family 2 (facilitated glucose transporter), member 1
    10866702Protein tyrosine phosphatase, nonreceptor type 13
  • Table 9

    Strongest genes selected from the quantized blue-cell-tumor data using the Mean method

    No.CloneIDGene description
    1325182Cadherin 2, N-cadherin (neuronal)
    236950Phosphofructokinase, liver
    3397963-Hydroxymethyl-3-methylglutaryl-Coenzyme A lyase
    4823598Proteasome (prosome, macropain) 26S subunit, non-ATPase, 12
    5770394Fc fragment of IgG, receptor, transporter, alpha
    61435862Antigen identified by monoclonal antibodies 12E7, F21, and O13
    7295985ESTs
    8377461Caveolin 1, caveolae protein, 22kD
    975254Cysteine and glycine-rich protein 2 (LIM domain only, smooth muscle)
    1050359Mannose phosphate isomerase
PreviousNext
Back to top
Molecular Cancer Therapeutics: 2 (7)
July 2003
Volume 2, Issue 7
  • Table of Contents
  • About the Cover

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Molecular Cancer Therapeutics article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Binarization of Microarray Data on the Basis of a Mixture Model1
(Your Name) has forwarded a page to you from Molecular Cancer Therapeutics
(Your Name) thought you would be interested in this article in Molecular Cancer Therapeutics.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Binarization of Microarray Data on the Basis of a Mixture Model1
Xiaobo Zhou, Xiaodong Wang and Edward R. Dougherty
Mol Cancer Ther July 1 2003 (2) (7) 679-684;

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Binarization of Microarray Data on the Basis of a Mixture Model1
Xiaobo Zhou, Xiaodong Wang and Edward R. Dougherty
Mol Cancer Ther July 1 2003 (2) (7) 679-684;
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results and Discussions
    • Appendix
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • Prediction of individual response to platinum/paclitaxel combination using novel marker genes in ovarian cancers
  • Low doses of cisplatin or gemcitabine plus Photofrin/photodynamic therapy: Disjointed cell cycle phase-related activity accounts for synergistic outcome in metastatic non–small cell lung cancer cells (H1299)
  • Mesenchymal progenitor cells as cellular vehicles for delivery of oncolytic adenoviruses
Show more Article
  • Home
  • Alerts
  • Feedback
  • Privacy Policy
Facebook  Twitter  LinkedIn  YouTube  RSS

Articles

  • Online First
  • Current Issue
  • Past Issues
  • Meeting Abstracts

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians

About MCT

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2021 by the American Association for Cancer Research.

Molecular Cancer Therapeutics
eISSN: 1538-8514
ISSN: 1535-7163

Advertisement